Abstract
A continuing problem with Inductive Logic Programming (ILP) [1] has been the poor handling of numbers. Constraint Inductive Logic Programming (CILP) aims to solve this problem. We propose a new approach to CILP, and implement a prototype of CILP system called BPU-CILP. In our approach, methods from pattern recognition, such as Fisher’s linear discriminant [2] and prototype-based partitional clustering [3], are introduced into CILP. BPU-CILP can generate various forms of polynomial constraints in multiple dimensions, without additional background knowledge. As results, a CLP program covering all positive examples and consistent with all negative examples can be automatically derived.
The work is supported by the Natural Science Foundation of China (60173014) and Beijing Municipal Natural Science Foundation (4022003).
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Zheng, L., Liu, C., Jia, D., Zhong, N. (2003). A New Approach to Constraint Inductive Logic Programming. In: Zhong, N., Raś, Z.W., Tsumoto, S., Suzuki, E. (eds) Foundations of Intelligent Systems. ISMIS 2003. Lecture Notes in Computer Science(), vol 2871. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-39592-8_50
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DOI: https://doi.org/10.1007/978-3-540-39592-8_50
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